Spherical Motion Vector Mapping for Enhanced Compression of 360-Degree Video in H.266/VVC

Also known as spherical or omnidirectional video, 360-degree video has become increasingly prevalent in autonomous vehicles (AVs), virtual reality (VR), augmented reality (AR), and immersive media applications. However, its compression poses unique challenges due to the projection from a spherical surface onto a 2D plane. Common projections like Equirectangular Projection (ERP) introduce significant geometric distortions, causing straight-line motions on the sphere to appear as curved trajectories in the 2D domain. Standard motion estimation in codecs like H.266 (Versatile Video Coding, VVC) relies on translational (linear) motion vectors, leading to poor prediction accuracy, large residual errors, and inflated bitrates for 360-degree video content. This paper proposes the implementation of Spherical Motion Vector (SMV) mapping in the pre-encoder stage. By performing motion vector calculation directly on the spherical coordinate system (θ, φ) before mapping to the 2D pixel grid (x, y), SMV enables accurate tracking of object motion across projection boundaries and warped regions. This approach minimizes residual data and improves overall compression efficiency. This paper details the mathematical foundations, integration with H.266, implementation considerations, and simulated performance gains. The proposed method builds on prior work in rotational and geodesic motion models while introducing pre-encoder spherical preprocessing for broader compatibility.